Abstract: Aiming at the reliability problem of solid-liquid rocket engine,an improved Bayesian network fault diagnosis system was designed,which can diagnose multiple faults of solidliquid rocket engine through networking autonomous logic reasoning.In order to extract the fault features of time series observation signals,a scheme combining the marching method with the kernel principal component analysis (KPCA) was proposed.And then based on the fuzzy c-means clustering algorithm (FCM),a fuzzy polymorphic Bayesian network was established to realize the fuzzy processing of the scale of observation signals,which improves the diagnosis ability of uncertain faults.The improved Bayesian network fault diagnosis system was established by Matlab/Simulink.The simulation results show that the improved algorithm can effectively diagnose the common faults of solid-liquid rocket engine,and can adapt to the condition of small sample set.Compared with the traditional Bayesian diagnosis algorithm,the average accuracy of fault diagnosis is improved by 20.9%.

Key words: solid-liquid rocket engine, fault diagnosis, Bayesian network, fuzzy c-mean clustering, nuclear principal component analysis